Python Cifar10AudioClassifier示例

示例#1

文件： cifar10_train.py 项目： shubhampachori12110095/mxnet-audio

def main():
    audio_path_label_pairs = load_audio_path_label_pairs()
    print('loaded: ', len(audio_path_label_pairs))

    classifier = Cifar10AudioClassifier(model_ctx=mxnet.gpu(0),
                                        data_ctx=mxnet.gpu(0))
    batch_size = 8
    epochs = 100
    history = classifier.fit(audio_path_label_pairs,
                             model_dir_path='./models',
                             batch_size=batch_size,
                             epochs=epochs,
                             checkpoint_interval=2)

示例#2

def main():
    audio_path_label_pairs = load_audio_path_label_pairs()
    shuffle(audio_path_label_pairs)
    print('loaded: ', len(audio_path_label_pairs))

    classifier = Cifar10AudioClassifier()
    classifier.load_model(model_dir_path='./models')

    for i in range(0, 20):
        audio_path, actual_label_id = audio_path_label_pairs[i]
        predicted_label_id = classifier.predict_class(audio_path)
        print(audio_path)
        predicted_label = gtzan_labels[predicted_label_id]
        actual_label = gtzan_labels[actual_label_id]

        print('predicted: ', predicted_label, 'actual: ', actual_label)

示例#3

def main():
    sys.path.append(patch_path('..'))

    audio_path_label_pairs = load_audio_path_label_pairs()
    print('loaded: ', len(audio_path_label_pairs))

    from mxnet_audio.library.cifar10 import Cifar10AudioClassifier
    classifier = Cifar10AudioClassifier(model_ctx=mxnet.gpu(0),
                                        data_ctx=mxnet.gpu(0))
    batch_size = 8
    epochs = 100
    history = classifier.fit(audio_path_label_pairs,
                             model_dir_path=patch_path('models'),
                             batch_size=batch_size,
                             epochs=epochs,
                             checkpoint_interval=2)

示例#4

def main():
    sys.path.append(patch_path('..'))
    audio_path_label_pairs = load_audio_path_label_pairs()
    shuffle(audio_path_label_pairs)
    print('loaded: ', len(audio_path_label_pairs))

    from mxnet_audio.library.cifar10 import Cifar10AudioClassifier
    classifier = Cifar10AudioClassifier()
    classifier.load_model(model_dir_path=patch_path('models'))

    for i in range(0, 20):
        audio_path, actual_label_id = audio_path_label_pairs[i]
        audio2vec = classifier.encode_audio(audio_path)
        print(audio_path)

        print('audio-to-vec: ', audio2vec)

示例#5

文件： cifar10_predict.py 项目： Basanta78/musicrs

def main():
    sys.path.append(patch_path(".."))

    audio_path_label_pairs = load_audio_path_label_pairs()
    shuffle(audio_path_label_pairs)
    print("loaded: ", len(audio_path_label_pairs))

    from mxnet_audio.library.cifar10 import Cifar10AudioClassifier
    from mxnet_audio.library.utility.gtzan_loader import gtzan_labels

    classifier = Cifar10AudioClassifier()
    classifier.load_model(model_dir_path=patch_path("models"))

    for i in range(0, 20):
        audio_path, actual_label_id = audio_path_label_pairs[i]
        predicted_label_id = classifier.predict_class(audio_path)
        print(audio_path)
        predicted_label = gtzan_labels[predicted_label_id]
        actual_label = gtzan_labels[actual_label_id]

        print("predicted: ", predicted_label, "actual: ", actual_label)